Adolescents' Weight Management Goals: Healthy and Unhealthy Associations with Eating Habits and Physical Activity
Bibliographic record
Abstract
BACKGROUND: This study aims to quantify associations between one's weight management goal and eating behaviors and physical activity among teenagers. METHODS: Weighted logistic regressions were performed predicting healthy behaviors from weight goal separately for boys and girls while controlling for age, body mass index, socioeconomic indicators, school type, and region using data from the 2010 to 2011 Enquête québécoise sur la santé des jeunes du secondaire, a survey of a representative sample of Quebec adolescents (N = 32,040). RESULTS: About 18%, 31%, 19%, and 34% of boys and 32%, 34%, 5%, and 25% of girls were respectively trying to lose weight, maintain their weight, gain weight, and not trying to do anything about their weight. Trying to lose weight was associated with lower likelihood of eating breakfast daily (boys: odds ratio [OR] = 0.72; 95% confidence interval [CI] = 0.61, 0.84 girls: OR = 0.61; 95% CI = 0.54, 0.70). Among girls, trying to lose weight was also associated with higher likelihood of consuming at least 5 portions of fruits and vegetables (OR = 1.20; 95% CI = 1.04, 1.37), lower likelihood of drinking sugar-sweetened beverages daily (OR = 0.77; 95% CI = 0.66, 0.90). Each weight-related goal was associated with unhealthy behaviors but most of them were also associated with adoption of healthier ones. CONCLUSION: Having a weight related goal cannot inherently be thought of as health promoting goal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".